Applied Computational Intelligence Lab & Trustworthy Syst University of Missouri - Rolla Systems of Systems: Cybersecurity Vulnerabilities and Opportunities Applied Mathematics for Deregulated Electric Power Systems: Optimization, Control, and Computational Intelligence Crystal City, November 2003 Donald Wunsch, ACIL Director Ann Miller, TSL Director
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Applied Computational Intelligence Lab & Trustworthy Systems Lab University of Missouri - Rolla Systems of Systems: Cybersecurity Vulnerabilities and Opportunities.
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Applied Computational Intelligence Lab & Trustworthy Systems Lab
University of Missouri - Rolla
Systems of Systems: Cybersecurity Vulnerabilities and Opportunities
Applied Mathematics for Deregulated Electric Power Systems:
Optimization, Control, and Computational IntelligenceCrystal City, November 2003
Donald Wunsch, ACIL DirectorAnn Miller, TSL Director
Venayagamoorthy– Ron Harley– Daryl Beetner– Danil Prokhorov– Raonak Uz-Zaman– Frank Harary
Personnel – Narayan Vishwanathan– Amit Agarwahl– Sam Mulder– Wenxin Liu– Nian Zhang– Alexander Novokhodko– Xindi Cai– Rohit Dua– Hu Xiao– Rui Xu– Brian Blaha– Paul Pigg– Arvind Rapka Nath– Qiang Yao– Kevin Bollum– Anjaya Shrestra– Karthik Balasubramanian– Pinar Demircan– Daniel Treat– Ian Downard– Eyad Salah Tagiedin– Ganesh Sridharan– Jason White– Krishnaprasad Balasubramanian– Dayle Majors– Nartaj Lakshminarasimhan– Siddarth Panchal– Robert Wayne Denier– Tongquan Wei– Jimish Doshi– Ravikiran Sharda
“system of systems”– Grown/evolved by adding components not
initially designed to be part of the system– Interdependencies not easily identified
Potential for cascading failures Potential for hidden robustness
Systems of Systems: Interdependencies
Trustworthiness Testing Market Demands Complexity Safety Life-Cycle Model Integration
Issues in Systems ofSystems
19841984 8686 9090 9292 9494 9696 199819988888
11
1010
100100
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co
mm
end
ed
dis
k s
pa
ce,
MB
Math package 1Math package 2Math package 3
Moore
’s L
aw
Source: IEEE Spectrum, January 1998
Complexity: Software Size Growth
Complexity: Software Size Growth
Complexity: Interdependencies
A graph representing almost 6 million lines of computer code. The graph contains approximately 33 thousand nodes and 34 thousand relations.
Source: NATO Report on Visualization, 1999.
Memory managementFile directory Access
I/O PrimitivesProcess Primitives
Process environmentMemory managementFile directory Access
I/O PrimitivesProcess Primitives
Process environmentMemory managementFile directory Access
I/O PrimitivesProcess Primitives
Process environmentMemory managementFile directory Access
I/O PrimitivesProcess Primitives
Process environment
Normalised Failure Rate, %0 5 10 15 20 25 30
LINUX
NT
Win-2000
Win-CE
Failure Rates – System Calls
(Source: Carnegie Mellon, CS Dept.)
Cascading failures Opportunities for errors Control, Communication, IT
– Pres. Commission on Critical Infrastructure Protection
– Particularly EMS & SCADA
Voltage Collapse
Effects of Complexity and Growth
High-Consequence
Even brief – expensive– Circuit fab: 20 min = ($30 M)
Recent large disruption caused deaths
Backups no guarantee– Well-known in software safety circles
Therac 25 classic example
At 0903 CST on 18 December 1997, at the Olathe (Kansas City) Air Route Traffic Control Center, a technician routed power through half of the redundant uninterruptible power system, preparatory to performing the annual preventive maintenance on the other half. Apparently the wrong board was pulled.
Complexity: Ripple Effect Example
Results: – Power only out for 4 minutes– Radar and communications working within 17
minutes– However, at least 300 planes were in the Olathe-
controlled airspace; domino effect: hundreds of flights canceled, diverted, or delayed with problems well into the evening.
Complexity: Ripple Effect Example
Not only did the Air Route Traffic Control Center have redundant systems, there were also standby generators and emergency batteries.
Yet, that December morning, these back-up systems were bypassed.
Why?
Back-up Systems Are Not a Guarantee
The back-up systems were bypassed because the system was in a maintenance state.
This particular combination of inputs was not anticipated to occur when the system was in maintenance mode.
Complex Interactions: States and Inputs
Tempting Target
Dramatic growth in number of knowledgeable experts
Potential to insert incorrect data or Denial of Service attacks
High leverage / low risk
Computational Intelligence Tools Can Help
Neural Net Intrusion Detection ADP Robust Controls Combinatorial Optimization for
reconfigurability
Intrusion Detection with Neural Nets
RBFNs can be used for misuse and anomaly detection using sequences of system calls
Data are obtained from 1998 DARPA Intrusion Detection Evaluation program
Also collaboration with Sandia Red Team
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
False positives
Tru
e p
osi
tive
s
accuracy=0.74
RBFNN Generalization on unknown test data
PNN + ADABOOST
Multi-Machine Power System Control
G1 G2
G3
1 2
3
4 5
900 KmExciter
AVR Vref2
Exciter
AVR
Vref1
Ve1 Ve2Vt1 Vt2
900 Km
900 Km
Pref1
1Governor Governor
Turbine Turbine
2
Pref2
Multi-Machine Power System with Conventional Controllers
Multi-Machine Power System with DHP Neurocontrollers
G1 G2
G3
1 2
3
4 5
900 KmExciter
Vref2
Exciter
Vref1
Vf1 Vf2Vt1
Vt2
900 Km
900 Km
Pref1
1
Turbine Turbine
2
Pref2
Neurocontoller
TDL TDL
P1
Neurocontoller
TDL TDL
P2
Vref1 Vref2
GovernorGovernor
DHP Critic Network Adaptation
PLANTTDL
ACTIONNeural
NetworkMODELNeural
Network
CRITICNeural
Network
CRITICNeural
Network
TDL
MODELNeural
Network
YrefY(t)
A(t))(
)(
tA
tU
(t+1)
)(^
1tY
)(^
tY
)(^
1tY
TDL
TDL
)(^
tY
)(^
1tY TDL
TDL )(^
2tY
++
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^1tY
1tJ
1t
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+---
EC2(t)
Terminal Voltage of Generator G2 for a 5% Step Change in its Desired Terminal
Voltage & Operating Point Changed
1 2 3 4 5 6 7 80.98
0.99
1
1.01
1.02
1.03
1.04
1.05
1.06
1.07
1.08
Time in seconds
Ter
min
al v
olta
ge in
pu
DHP
AVR
Speed Deviation of Generator G2 - Operating Point Changed
0 1 2 3 4 5 6 7 8-1.5
-1
-0.5
0
0.5
1
1.5x 10
-3
Time in seconds
Sp
ee
d d
evi
atio
n o
f G
1 in
pu
Conventional
Neurocontroller
Traveling Salesman Problem
Great benchmark NP – complete
– Maps to other NP – complete problems Public databases
Big need – get learning capability of NN without brittleness of other techniques.
Paper Method Largest
Instance
Quality
(percent
excess over
optimal )
Test bed
[11] 1st 100 14.6% NS
[13] 1st 100 14% NS
[10] 1st 400 NR NS
[5] 2nd 532 6.8% TSPLIB
[12] 1st 1000 NR NS
[16] 2nd 1000 NR NS
[15] 1st 2392 5% TSPLIB
[17] 2nd 2392 9% TSPLIB
[2] 1st 10000 NR NS
[4] 1st 11849 17.4% TSPLIB
Previous contributions -- disappointing
Clustered Traveling Salesman
Divide problem into clusters using ART in O(n)
Use Lin-Kernighan algorithm for global tour
Use Lin-Kernighan algorithm for local tours
Merge local tours in O(n) time Global operations limited to O(n) time
Algorithm Overview
Read problem from file O(n)
ART O(n lg n)
cluster
cluster
cluster
LK O(k2.2)
LK O(k2.2)
LK O(k2.2)
Merge Clusters O(n)
Result
Implemented in C++ thread-safe code Uses Windows threads for parallelism Operating System-specific code isolated to one
file Should be easy to port to other parallel
systems
Implementation
#cities Tour Length 1P Time 2P Time Vig factor % off Speedup 1000 2.58E+07 0.422 0.281 0.7 10.40% 1.50